Package: idiographic 0.1.0

Mohammed Saqr

idiographic: Network Estimation from Intensive Longitudinal Data

Person-specific and within-person network estimation from intensive longitudinal and panel data. Provides preprocessing audits, edge-stability diagnostics, model-comparison reports, rolling forecast validation, rolling ordinary and graphical vector autoregression, ordinary vector autoregression (VAR), graphical vector autoregression (graphical VAR), multilevel vector autoregression (mlVAR), native Bayesian VAR and multilevel VAR that statistically reproduce 'Mplus' Dynamic Structural Equation Modeling (DSEM) output without requiring 'Mplus', unified Structural Equation Modeling (uSEM), and Group Iterative Multiple Model Estimation (GIMME) as clean-room implementations. Split out of the 'Nestimate' package so the idiographic time-series methods carry their own dependencies. Results have tidy accessors and 'cograph_network' plotting support.

Authors:Mohammed Saqr [aut, cre]

idiographic_0.1.0.tar.gz
idiographic_0.1.0.zip(r-4.7)idiographic_0.1.0.zip(r-4.6)idiographic_0.1.0.zip(r-4.5)
idiographic_0.1.0.tgz(r-4.6-any)idiographic_0.1.0.tgz(r-4.5-any)
idiographic_0.1.0.tar.gz(r-4.7-any)idiographic_0.1.0.tar.gz(r-4.6-any)
idiographic_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
idiographic/json (API)

# Install 'idiographic' in R:
install.packages('idiographic', repos = c('https://mohsaqr.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/mohsaqr/idiographic/issues

Datasets:
  • srl - Self-regulated learning intensive longitudinal data

On CRAN:

Conda:

4.04 score 23 exports 19 dependencies

Last updated from:8bcb5b760c. Checks:7 ERROR, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64ERROR1083
source / vignettesOK440
linux-release-x86_64ERROR1077
macos-release-arm64ERROR646
macos-oldrel-arm64ERROR691
windows-develERROR1243
windows-releaseERROR890
windows-oldrelERROR1157
wasm-releaseOK180

Exports:as_netobjectaudit_preprocessbuild_gimmebuild_mlvarbuild_mlvar_bayesbuild_mlvar_mplusbuild_usembuild_varbuild_var_bayesbuild_var_eachcoefscompare_idiographicedgesestimate_stabilityextract_edgesgraphical_vargraphical_var_eachmatricesnodesplot_gimmerolling_graphical_varrolling_varvalidate_forecast

Dependencies:bootlatticelavaanlme4MASSMatrixminqamnormtnlmenloptrnumDerivpbivnormquadprogrbibutilsRcppRcppEigenRdpackreformulasrlang

Package overview: a clean-room methods tour
Introduction | Clean-room implementation | The empirical example | 1. Preprocessing audit — audit_preprocess() | 2. Ordinary VAR — build_var() | 3. Graphical VAR — graphical_var() | 4. One network per person — build_var_each() / graphical_var_each() | 5. Multilevel VAR — build_mlvar() | 6. Bayesian multilevel VAR / DSEM — build_mlvar_bayes() | 7. Bayesian single-subject VAR — build_var_bayes() | 8. The Mplus backend — build_mlvar_mplus() | 9. Unified SEM — build_usem() | 10. GIMME — build_gimme() | 11. Rolling networks — rolling_var() / rolling_graphical_var() | 12. Model comparison — compare_idiographic() | One grammar for every result | References
Rendered fromclean-room-methods.Rmdusingknitr::rmarkdown

Last update: 2026-07-01
Started: 2026-07-01

Stability and forecast validation
Edge stability | Forecast validation
Rendered fromstability-and-forecasting.Rmdusingknitr::rmarkdown

Last update: 2026-07-01
Started: 2026-07-01

Getting started with idiographic
The data | Preprocessing audit | Where to go next
Rendered fromidiographic.Rmdusingknitr::rmarkdown

Last update: 2026-07-01
Started: 2026-07-01

Comparing methods
Rendered fromcomparing-methods.Rmdusingknitr::rmarkdown

Last update: 2026-07-01
Started: 2026-07-01

Ordinary VAR
Fit one person | Tidy tables | Plot
Rendered fromordinary-var.Rmdusingknitr::rmarkdown

Last update: 2026-07-01
Started: 2026-07-01

Graphical VAR
Fit one person | Tidy tables | Plot
Rendered fromgraphical-var.Rmdusingknitr::rmarkdown

Last update: 2026-07-01
Started: 2026-07-01

Subject-by-subject networks
One OLS VAR per person | One graphical VAR per person
Rendered fromsubject-networks.Rmdusingknitr::rmarkdown

Last update: 2026-07-01
Started: 2026-07-01

Multilevel VAR (mlVAR)
Fit | Tidy tables | Plot
Rendered frommlvar.Rmdusingknitr::rmarkdown

Last update: 2026-07-01
Started: 2026-07-01

Unified SEM (uSEM)
Tidy tables | Plot
Rendered fromusem.Rmdusingknitr::rmarkdown

Last update: 2026-07-01
Started: 2026-07-01

GIMME
Tidy tables | Plot
Rendered fromgimme.Rmdusingknitr::rmarkdown

Last update: 2026-07-01
Started: 2026-07-01

Rolling networks
Rolling OLS VAR | Rolling graphical VAR
Rendered fromrolling-networks.Rmdusingknitr::rmarkdown

Last update: 2026-07-01
Started: 2026-07-01

Readme and manuals

Help Manual

Help pageTopics
Coerce to a netobjectas_netobject
Coerce a gvar_result to plottable netobjectsas_netobject.gvar_result
Plottable netobject(s) from a GIMME fitas_netobject.net_gimme
Plottable netobjects from an mlVAR fitas_netobject.net_mlvar
Coerce a var_bayes_result to plottable netobjectsas_netobject.var_bayes_result
Audit preprocessing and lag constructionaudit_preprocess
GIMME: Group Iterative Multiple Model Estimationbuild_gimme
Build a Multilevel Vector Autoregression (mlVAR) networkbuild_mlvar
Build a Bayesian multilevel VAR network (Mplus DSEM equivalent)build_mlvar_bayes
Build an Mplus-backed multilevel VAR networkbuild_mlvar_mplus
Build a user-specified unified SEM networkbuild_usem
Build an ordinary least-squares VAR networkbuild_var
Build a Bayesian VAR(1) network (unregularized Mplus-equivalent)build_var_bayes
Fit an ordinary least-squares VAR for every subjectbuild_var_each
Tidy coefficients from a fitted mlvar modelcoefs coefs.default coefs.gvar_result coefs.net_gimme coefs.net_mlvar coefs.net_mlvar_bayes coefs.net_usem coefs.var_bayes_result coefs.var_result
Compare idiographic estimators on one datasetcompare_idiographic
Tidy edge table for any idiographic resultedges edges.gvar_result edges.netobject edges.netobject_group edges.net_gimme edges.net_mlvar edges.net_usem edges.var_result
Estimate edge stability by block resampling (experimental)estimate_stability
Tidy edge table from a network objectextract_edges
Graphical VAR Estimationgraphical_var
Fit a graphical VAR for every subjectgraphical_var_each
Print model matrices for idiographic resultsmatrices matrices.cograph_network matrices.default matrices.gvar_result matrices.model_comparison matrices.netobject matrices.netobject_group matrices.net_gimme matrices.net_mlvar matrices.net_usem matrices.preprocess_audit matrices.rolling_gvar_result matrices.rolling_var_result matrices.stability_result matrices.var_result
Tidy per-node strength table for any idiographic resultnodes nodes.gvar_result nodes.netobject nodes.netobject_group nodes.net_gimme nodes.net_mlvar nodes.net_usem nodes.var_result
Faithful GIMME network plot (the 'gimme'-package convention, via cograph)plot_gimme
Plot an idiographic network resultplot.gvar_list plot.gvar_result plot.net_gimme plot.net_mlvar plot.net_usem plot.rolling_gvar_result plot.rolling_var_result plot.stability_result plot.var_bayes_result plot.var_list plot.var_result plot_idiographic
Print method for forecast validation resultsprint.forecast_result
Print a list of per-subject graphical VARsprint.gvar_list
Print Method for gvar_resultprint.gvar_result
Print method for model comparisonsprint.model_comparison
Print Method for net_gimmeprint.net_gimme
Print method for net_mlvarprint.net_mlvar
Print method for net_mlvar_bayesprint.net_mlvar_bayes
Print method for uSEM fitsprint.net_usem
Print method for preprocessing auditsprint.preprocess_audit
Print method for rolling graphical VAR resultsprint.rolling_gvar_result
Print method for rolling VAR resultsprint.rolling_var_result
Print method for stability resultsprint.stability_result
Print method for var_bayes_resultprint.var_bayes_result
Print a list of per-subject ordinary VARsprint.var_list
Print method for ordinary VAR fitsprint.var_result
Estimate rolling-window graphical VAR networksrolling_graphical_var
Estimate rolling-window ordinary VAR networksrolling_var
Self-regulated learning intensive longitudinal data (Chapter 20)srl
Summary Method for gvar_resultsummary.gvar_result
Summary Method for net_gimmesummary.net_gimme
Summary method for net_mlvarsummary.net_mlvar
Summary method for uSEM fitssummary.net_usem
Summary method for var_bayes_resultsummary.var_bayes_result
Summary method for ordinary VAR fitssummary.var_result
Validate one-step forecasts from idiographic VAR models (experimental)validate_forecast